{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这是学习Python的Numpy，Pandas，Matplotlib的笔记。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd # type: ignore\n",
    "import numpy as np # type: ignore\n",
    "import matplotlib.pyplot as plt # type: ignore\n",
    "\n",
    "# 定义x轴的数据点，这里是一个包含0和6的数组\n",
    "xpoints = np.array([0, 6])\n",
    "\n",
    "# 定义y轴的数据点，这里是一个包含0和260的数组\n",
    "ypoints = np.array([0, 260])\n",
    "\n",
    "# 使用matplotlib的plot函数绘制折线图，xpoints为x轴数据，ypoints为y轴数据\n",
    "plt.plot(xpoints, ypoints)\n",
    "\n",
    "# 显示绘制的图表\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # type: ignore\n",
    "import numpy as np # type: ignore\n",
    "import matplotlib.pyplot as plt # type: ignore\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
